A Comparative Study of Face Detection Algorithms for Masked Face Detection
This addresses the problem of occluded face detection for applications like security and health during the COVID-19 pandemic, but it is incremental as it focuses on comparing existing methods.
The study evaluated how well existing face detection algorithms perform on masked faces, finding a lack of evidence in this area and comparing their performances to identify contributing factors.
Contemporary face detection algorithms have to deal with many challenges such as variations in pose, illumination, and scale. A subclass of the face detection problem that has recently gained increasing attention is occluded face detection, or more specifically, the detection of masked faces. Three years on since the advent of the COVID-19 pandemic, there is still a complete lack of evidence regarding how well existing face detection algorithms perform on masked faces. This article first offers a brief review of state-of-the-art face detectors and detectors made for the masked face problem, along with a review of the existing masked face datasets. We evaluate and compare the performances of a well-representative set of face detectors at masked face detection and conclude with a discussion on the possible contributing factors to their performance.